{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from multiprocessing import Process\n",
    "from time import sleep\n",
    "import time\n",
    "import numpy as np\n",
    "import sklearn.datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.ensemble.forest import RandomForestClassifier\n",
    "from kfoldwrapper import TFKFoldWrapper, SKKFoldWrapper, RFKFoldWrapper, evaluate_performance\n",
    "from load_adult import load_adult\n",
    "from load_yeast import load_yeast\n",
    "from load_letter import load_letter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "((16000, 16), (4000, 16))\n",
      "((16000,), (4000,))\n",
      "Num Classes: 26\n"
     ]
    }
   ],
   "source": [
    "# X, y = sklearn.datasets.load_digits(return_X_y=True)\n",
    "# x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
    "# x_train = x_train.astype(np.float32)\n",
    "# x_test = x_test.astype(np.float32)\n",
    "# print(x_train.shape, x_test.shape)\n",
    "\n",
    "x_train, x_test, y_train, y_test = load_letter()\n",
    "print(x_train.shape, x_test.shape)\n",
    "print(y_train.shape, y_test.shape)\n",
    "y_train = y_train.astype(np.int)\n",
    "y_test = y_test.astype(np.int)\n",
    "print(\"Num Classes: {}\".format(len(set(y_test))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_train = 16000   # 1038      # 32561\n",
    "n_test = 4000     # 446       # 16281\n",
    "num_features = x_test.shape[-1]\n",
    "if len(y_test.shape) == 1:\n",
    "    num_classes = len(set(y_test))\n",
    "else:\n",
    "    num_classes = y_test.shape[-1]\n",
    "n_estimators = 50\n",
    "max_leaf_nodes = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "number_workers = 2\n",
    "worker_ips = []\n",
    "for i in range(number_workers):\n",
    "    worker_ips.append(\"localhost:333{}\".format(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cluster = tf.train.ClusterSpec({\n",
    "    \"worker\": worker_ips,\n",
    "    \"ps\": [\n",
    "        \"localhost:3376\"\n",
    "    ]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Worker side\n",
    "def get_prob(data_type=\"train\", idx=0):\n",
    "    with tf.variable_scope(\"prob_{}\".format(data_type), reuse=True):\n",
    "        x = tf.get_variable(name='x_{}'.format(idx),\n",
    "                            initializer=tf.zeros((594, 10), dtype=np.float32),\n",
    "                            dtype=np.float32)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def parameter_server(num_workers=2,\n",
    "                     n_train=32561,\n",
    "                     n_test=16281,\n",
    "                     num_features=113,\n",
    "                     num_classes=2):\n",
    "    with tf.device(\"/job:ps/task:0\"):\n",
    "        cont = tf.Variable([True for i in range(num_workers)], name='continue')\n",
    "        shut_down = tf.Variable(False, name='shutdown')\n",
    "        concat = tf.Variable(False, name='concat')\n",
    "        layer_train_prob = tf.Variable(np.zeros((n_train, num_classes)), name='layer_train_prob')\n",
    "        layer_test_prob = tf.Variable(np.zeros((n_test, num_classes)), name='layer_test_prob')\n",
    "        ready = tf.Variable([False for _ in range(num_workers)], name='ready')\n",
    "        prob_train_xs = [None for _ in range(num_workers)]\n",
    "        with tf.variable_scope(\"prob_train\"):\n",
    "            for i in range(num_workers):\n",
    "                prob_train_xs[i] = tf.get_variable(name='x_{}'.format(i),\n",
    "                                                   initializer=tf.zeros((n_train, num_classes), dtype=np.float32),\n",
    "                                                   dtype=np.float32)\n",
    "        prob_test_xs = [None for _ in range(num_workers)]\n",
    "        with tf.variable_scope(\"prob_test\"):\n",
    "            for i in range(num_workers):\n",
    "                prob_test_xs[i] = tf.get_variable(name='x_{}'.format(i),\n",
    "                                                  initializer=tf.zeros((n_test, num_classes), dtype=np.float32),\n",
    "                                                  dtype=np.float32)\n",
    "\n",
    "    server = tf.train.Server(cluster,\n",
    "                             job_name=\"ps\",\n",
    "                             task_index=0)\n",
    "    sess = tf.Session(target=server.target)\n",
    "\n",
    "    print(\"Parameter server: waiting for cluster connection...\")\n",
    "    sess.run(tf.report_uninitialized_variables())\n",
    "    print(\"Parameter server: cluster ready!\")\n",
    "\n",
    "    print(\"Parameter server: initializing variables...\")\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    print(\"Parameter server: variables initialized\")\n",
    "    \n",
    "    train_acc_records = []\n",
    "    test_acc_records = []\n",
    "    best_test_acc = 0.0\n",
    "    best_layer = 0\n",
    "    early_stopping = False\n",
    "\n",
    "    num_layer = 0\n",
    "    while True:\n",
    "        while not sess.run(ready).all():\n",
    "            ready_value = sess.run(ready)\n",
    "            # print(\"ready indicator: \", ready_value)\n",
    "            sleep(1.0)\n",
    "        print(\"All Ready!!!\")\n",
    "\n",
    "#         for i in range(2):\n",
    "#             print(\"prob_train_xs[{}] = {}\".format(i, sess.run(prob_train_xs[i])))\n",
    "#             print(\"prob_test_xs[{}] = {}\".format(i, sess.run(prob_test_xs[i])))\n",
    "\n",
    "        # print(\"Layer train Probability: {}\".format(sess.run(layer_train_prob)))\n",
    "        # print(\"Layer test  Probability: {}\".format(sess.run(layer_test_prob)))\n",
    "        train_acc = evaluate_performance(sess.run(layer_train_prob), y_train)\n",
    "        test_acc = evaluate_performance(sess.run(layer_test_prob), y_test)\n",
    "        print(\"LAYER train={}, test={}\".format(train_acc, test_acc))\n",
    "        train_acc_records.append(train_acc)\n",
    "        test_acc_records.append(test_acc)\n",
    "        if test_acc > best_test_acc:\n",
    "            best_test_acc = test_acc\n",
    "            best_layer = num_layer\n",
    "        if num_layer - best_layer >= 4:\n",
    "            early_stopping = True\n",
    "        each_train_acc = [evaluate_performance(sess.run(prob_train_xs[i]), y_train) for i in range(num_workers)]\n",
    "        each_test_acc = [evaluate_performance(sess.run(prob_test_xs[i]), y_test) for i in range(num_workers)]\n",
    "        print(\"Every train acc = {}\".format(each_train_acc))\n",
    "        print(\"Every test  acc = {}\".format(each_test_acc))\n",
    "        num_layer += 1\n",
    "        sess.run(ready.assign([False for i in range(num_workers)]))\n",
    "        if num_layer > 0:     # to concat\n",
    "            sess.run(concat.assign(True))\n",
    "        if early_stopping or num_layer > 30:             # judge earlystopping, or max_layer encountered\n",
    "            print(\"Early Stopped, best_acc = {}, best_layer = {}\".format(best_test_acc, best_layer))\n",
    "            sess.run(shut_down.assign(True))\n",
    "            break\n",
    "        sess.run(cont.assign([True for i in range(num_workers)]))\n",
    "        print(\"cont = {}\".format(sess.run(cont)))\n",
    "        print(\"Continue Training Layer {} ......\".format(num_layer))\n",
    "\n",
    "    print(\"Parameter server: blocking...\")\n",
    "    server.join()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def worker(num_workers,\n",
    "           worker_n,\n",
    "           n_train=32561,\n",
    "           n_test=16281,\n",
    "           num_features=113,\n",
    "           num_classes=2):\n",
    "    with tf.device(\"/job:ps/task:0\"):\n",
    "        cont = tf.Variable([True for i in range(num_workers)], name='continue')\n",
    "        shut_down = tf.Variable(False, name='shutdown')\n",
    "        concat = tf.Variable(False, name='concat')\n",
    "        layer_train_prob = tf.Variable(np.zeros((n_train, num_classes)), name='layer_train_prob')\n",
    "        layer_test_prob = tf.Variable(np.zeros((n_test, num_classes)), name='layer_test_prob')\n",
    "        ready = tf.Variable([False for _ in range(num_workers)], name='ready')\n",
    "        prob_train_xs = [None for _ in range(num_workers)]\n",
    "        with tf.variable_scope(\"prob_train\"):\n",
    "            for i in range(num_workers):\n",
    "                prob_train_xs[i] = tf.get_variable(name='x_{}'.format(i),\n",
    "                                                   initializer=tf.zeros((n_train, num_classes), dtype=np.float32),\n",
    "                                                   dtype=np.float32)\n",
    "        prob_test_xs = [None for _ in range(num_workers)]\n",
    "        with tf.variable_scope(\"prob_test\"):\n",
    "            for i in range(num_workers):\n",
    "                prob_test_xs[i] = tf.get_variable(name='x_{}'.format(i),\n",
    "                                                  initializer=tf.zeros((n_test, num_classes), dtype=np.float32),\n",
    "                                                  dtype=np.float32)\n",
    "\n",
    "    server = tf.train.Server(cluster,\n",
    "                             job_name=\"worker\",\n",
    "                             task_index=worker_n)\n",
    "    sess = tf.Session(target=server.target)\n",
    "\n",
    "    print(\"Worker %d: waiting for cluster connection...\" % worker_n)\n",
    "    sess.run(tf.report_uninitialized_variables())\n",
    "    print(\"Worker %d: cluster ready!\" % worker_n)\n",
    "\n",
    "    while sess.run(tf.report_uninitialized_variables()).any():\n",
    "        print(\"Worker %d: waiting for variable initialization...\" % worker_n)\n",
    "        sleep(1.0)\n",
    "    print(\"Worker %d: variables initialized\" % worker_n)\n",
    "    \n",
    "    num_layer = 0\n",
    "    while True:\n",
    "        print(\"[Worker {}] Start training Layer {} ......\".format(worker_n, num_layer))\n",
    "\n",
    "        # now continue, close continue tag ============================================\n",
    "        indices = tf.constant([[worker_n],])\n",
    "        updates = tf.constant([False])\n",
    "        set_cont = cont.scatter_nd_update(indices, updates)\n",
    "        sess.run(set_cont)\n",
    "        # =============================================================================\n",
    "        \n",
    "        # concat ======================================================================\n",
    "        need_concat = sess.run(concat)\n",
    "        print(\"Need Concat ? {}\".format(need_concat))\n",
    "        if need_concat:\n",
    "            true_x_train = np.hstack((x_train, ) + tuple(sess.run(prob_train_xs[i]) for i in range(num_workers)))\n",
    "            true_x_test = np.hstack((x_test, ) + tuple(sess.run(prob_test_xs[i]) for i in range(num_workers)))\n",
    "        else:\n",
    "            true_x_train = x_train\n",
    "            true_x_test = x_test\n",
    "        print(\"true_x_train, true_x_test shape = {}, {}\".format(true_x_train.shape, true_x_test.shape))\n",
    "        print(\"true_x_train = {}\".format(true_x_train))\n",
    "        # end concat ==================================================================\n",
    "\n",
    "        est_name = 'layer-{}-estimator-{}-{}folds'.format(num_layer, worker_n, 3)\n",
    "        seed = (42 + hash(\"[estimator] {}\".format(est_name))) % 1000000007\n",
    "\n",
    "        # Tensorflow KFoldWrapper =====================================================\n",
    "        est_args = {'num_classes': num_classes, 'num_features': true_x_train.shape[-1], 'regression': False,\n",
    "                    'num_trees': n_estimators, 'max_nodes': max_leaf_nodes,\n",
    "                    'base_random_seed': None}\n",
    "        from kfoldwrapper import TFKFoldWrapper\n",
    "        kfw = TFKFoldWrapper\n",
    "        # =============================================================================\n",
    "\n",
    "        # SKlearn KFoldWrapper ========================================================\n",
    "#         est_args = {'n_estimators': n_estimators, 'max_features': 'auto', 'max_leaf_nodes': max_leaf_nodes,\n",
    "#                     'random_state': None}\n",
    "        \n",
    "#         from kfoldwrapper import TFKFoldWrapper, SKKFoldWrapper, RFKFoldWrapper\n",
    "#         if worker_n < number_workers/2:\n",
    "#             kfw = SKKFoldWrapper\n",
    "#         else:\n",
    "#             kfw = RFKFoldWrapper\n",
    "        # =============================================================================\n",
    "\n",
    "        y_proba_train, y_proba_test = kfw(name=est_name, n_folds=3,\n",
    "                                          task='classification',\n",
    "                                          seed=seed, dtype=np.float32,\n",
    "                                          est_args=est_args, cv_seed=seed).fit_transform(\n",
    "            X=true_x_train, y=y_train, x_test=true_x_test, y_test=y_test)\n",
    "        # =============================================================================\n",
    "\n",
    "        x = get_prob(\"test\", worker_n)\n",
    "        # print(\"x.name = {}\".format(x.name))\n",
    "        sess.run(x.assign(y_proba_test))\n",
    "        sess.run(layer_test_prob.assign_add(y_proba_test))\n",
    "        # ==============================================================================\n",
    "\n",
    "        x = get_prob(\"train\", worker_n)\n",
    "        # print(\"x.name = {}\".format(x.name))\n",
    "        sess.run(x.assign(y_proba_train))\n",
    "        sess.run(layer_train_prob.assign_add(y_proba_train))\n",
    "        # ==============================================================================\n",
    "\n",
    "        # ready ========================================================================\n",
    "        indices = tf.constant([[worker_n],])\n",
    "        updates = tf.constant([True])\n",
    "        set_ready = ready.scatter_nd_update(indices, updates)\n",
    "        sess.run(set_ready)\n",
    "        # ==============================================================================\n",
    "        \n",
    "        # wait the indicator of the next run ===========================================\n",
    "        print(\"[Worker {}] wait the indicator of the next run ...\".format(worker_n))\n",
    "        is_shutdown = False\n",
    "        while not sess.run(cont)[worker_n]:  # while not continue\n",
    "            time.sleep(1.0)\n",
    "            print(\"cont indicator: \", sess.run(cont)[worker_n])\n",
    "            if sess.run(shut_down):\n",
    "                is_shutdown = True\n",
    "                break\n",
    "        if is_shutdown:\n",
    "            print(\"Worker %d shutting down...\" % worker_n)\n",
    "            break\n",
    "        num_layer += 1\n",
    "\n",
    "    print(\"Worker %d: blocking...\" % worker_n)\n",
    "    server.join()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "__init__() got an unexpected keyword argument 'daemon'",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-0f77e923d6f7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mps_proc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mProcess\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparameter_server\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumber_workers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_features\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_classes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdaemon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mw_procs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumber_workers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     w_proc = Process(target=worker,\n\u001b[1;32m      5\u001b[0m                      \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumber_workers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_features\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_classes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'daemon'"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "ps_proc = Process(target=parameter_server, args=(number_workers, n_train, n_test, num_features, num_classes, ), daemon=True)\n",
    "w_procs = []\n",
    "for i in range(number_workers):\n",
    "    w_proc = Process(target=worker,\n",
    "                     args=(number_workers, i, n_train, n_test, num_features, num_classes, ),\n",
    "                     daemon=True)\n",
    "    w_procs.append(w_proc)\n",
    "    # w2_proc = Process(target=worker, args=(2, 1, n_train, n_test, num_features, num_classes, ), daemon=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter server: waiting for cluster connection...\n",
      "Parameter server: cluster ready!\n",
      "Parameter server: initializing variables...\n",
      "Parameter server: variables initialized\n"
     ]
    }
   ],
   "source": [
    "ps_proc.start()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Worker 1: waiting for cluster connection...\n",
      "Worker 2: waiting for cluster connection...\n",
      "Worker 3: waiting for cluster connection...\n",
      "Worker 0: waiting for cluster connection...\n",
      "Worker 4: waiting for cluster connection...\n",
      "Worker 5: waiting for cluster connection...\n",
      "Worker 6: waiting for cluster connection...\n",
      "Worker 7: waiting for cluster connection...\n",
      "Worker 0: cluster ready!\n",
      "Worker 5: cluster ready!\n",
      "Worker 1: cluster ready!\n",
      "Worker 3: cluster ready!\n",
      "Worker 2: cluster ready!\n",
      "Worker 4: cluster ready!\n",
      "Worker 6: cluster ready!\n",
      "Worker 7: cluster ready!\n",
      "Worker 5: waiting for variable initialization...\n",
      "Worker 0: waiting for variable initialization...\n",
      "Worker 3: waiting for variable initialization...\n",
      "Worker 1: waiting for variable initialization...\n",
      "Worker 2: waiting for variable initialization...\n",
      "Worker 4: waiting for variable initialization...\n",
      "Worker 6: waiting for variable initialization...\n",
      "Worker 7: waiting for variable initialization...\n",
      "Worker 5: variables initialized\n",
      "[Worker 5] Start training Layer 0 ......\n",
      "Worker 1: variables initialized\n",
      "[Worker 1] Start training Layer 0 ......\n",
      "Worker 0: variables initialized\n",
      "Worker 3: variables initialized\n",
      "[Worker 0] Start training Layer 0 ......\n",
      "[Worker 3] Start training Layer 0 ......\n",
      "Need Concat ? False\n",
      "true_x_train, true_x_test shape = (16000, 16), (4000, 16)\n",
      "Worker 6: variables initialized\n",
      "true_x_train = [[ 2.  8.  3. ...  8.  0.  8.]\n",
      " [ 5. 12.  3. ...  8.  4. 10.]\n",
      " [ 4. 11.  6. ...  7.  3.  9.]\n",
      " ...\n",
      " [ 8. 14.  7. ...  7.  5.  8.]\n",
      " [ 4.  7.  5. ...  8.  5.  8.]\n",
      " [ 2.  1.  3. ...  9.  4. 10.]]Worker 4: variables initialized\n",
      "[Worker 6] Start training Layer 0 ......\n",
      "\n",
      "Worker 2: variables initialized\n",
      "[Worker 2] Start training Layer 0 ......\n",
      "[Worker 4] Start training Layer 0 ......\n",
      "Need Concat ? False\n",
      "true_x_train, true_x_test shape = (16000, 16), (4000, 16)\n",
      "true_x_train = [[ 2.  8.  3. ...  8.  0.  8.]\n",
      " [ 5. 12.  3. ...  8.  4. 10.]\n",
      " [ 4. 11.  6. ...  7.  3.  9.]\n",
      " ...\n",
      " [ 8. 14.  7. ...  7.  5.  8.]\n",
      " [ 4.  7.  5. ...  8.  5.  8.]\n",
      " [ 2.  1.  3. ...  9.  4. 10.]]\n",
      "Need Concat ? False\n",
      "Need Concat ? False\n",
      "true_x_train, true_x_test shape = (16000, 16), (4000, 16)\n",
      "Need Concat ? False\n",
      "true_x_train = [[ 2.  8.  3. ...  8.  0.  8.]\n",
      " [ 5. 12.  3. ...  8.  4. 10.]\n",
      " [ 4. 11.  6. ...  7.  3.  9.]\n",
      " ...\n",
      " [ 8. 14.  7. ...  7.  5.  8.]\n",
      " [ 4.  7.  5. ...  8.  5.  8.]\n",
      " [ 2.  1.  3. ...  9.  4. 10.]]Need Concat ? False\n",
      "\n",
      "true_x_train, true_x_test shape = (16000, 16), (4000, 16)\n",
      "true_x_train, true_x_test shape = (16000, 16), (4000, 16)\n",
      "true_x_train, true_x_test shape = (16000, 16), (4000, 16)\n",
      "Worker 7: variables initialized\n",
      "true_x_train = [[ 2.  8.  3. ...  8.  0.  8.]\n",
      " [ 5. 12.  3. ...  8.  4. 10.]\n",
      " [ 4. 11.  6. ...  7.  3.  9.]\n",
      " ...\n",
      " [ 8. 14.  7. ...  7.  5.  8.]\n",
      " [ 4.  7.  5. ...  8.  5.  8.]\n",
      " [ 2.  1.  3. ...  9.  4. 10.]]true_x_train = [[ 2.  8.  3. ...  8.  0.  8.]\n",
      " [ 5. 12.  3. ...  8.  4. 10.]\n",
      " [ 4. 11.  6. ...  7.  3.  9.]\n",
      " ...\n",
      " [ 8. 14.  7. ...  7.  5.  8.]\n",
      " [ 4.  7.  5. ...  8.  5.  8.]\n",
      " [ 2.  1.  3. ...  9.  4. 10.]][Worker 7] Start training Layer 0 ......\n",
      "\n",
      "true_x_train = [[ 2.  8.  3. ...  8.  0.  8.]\n",
      " [ 5. 12.  3. ...  8.  4. 10.]\n",
      " [ 4. 11.  6. ...  7.  3.  9.]\n",
      " ...\n",
      " [ 8. 14.  7. ...  7.  5.  8.]\n",
      " [ 4.  7.  5. ...  8.  5.  8.]\n",
      " [ 2.  1.  3. ...  9.  4. 10.]]\n",
      "\n",
      "Need Concat ? False\n",
      "true_x_train, true_x_test shape = (16000, 16), (4000, 16)\n",
      "true_x_train = [[ 2.  8.  3. ...  8.  0.  8.]\n",
      " [ 5. 12.  3. ...  8.  4. 10.]\n",
      " [ 4. 11.  6. ...  7.  3.  9.]\n",
      " ...\n",
      " [ 8. 14.  7. ...  7.  5.  8.]\n",
      " [ 4.  7.  5. ...  8.  5.  8.]\n",
      " [ 2.  1.  3. ...  9.  4. 10.]]\n",
      "Need Concat ? False\n",
      "true_x_train, true_x_test shape = (16000, 16), (4000, 16)\n",
      "true_x_train = [[ 2.  8.  3. ...  8.  0.  8.]\n",
      " [ 5. 12.  3. ...  8.  4. 10.]\n",
      " [ 4. 11.  6. ...  7.  3.  9.]\n",
      " ...\n",
      " [ 8. 14.  7. ...  7.  5.  8.]\n",
      " [ 4.  7.  5. ...  8.  5.  8.]\n",
      " [ 2.  1.  3. ...  9.  4. 10.]]\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/tensor_forest/client/random_forest.py:130: multi_class_head (from tensorflow.contrib.learn.python.learn.estimators.head) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please switch to tf.contrib.estimator.*_head.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:1180: BaseEstimator.__init__ (from tensorflow.contrib.learn.python.learn.estimators.estimator) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please replace uses of any Estimator from tf.contrib.learn with an Estimator from tf.estimator.*\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:428: RunConfig.__init__ (from tensorflow.contrib.learn.python.learn.estimators.run_config) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "When switching to tf.estimator.Estimator, use tf.estimator.RunConfig instead.\n",
      "INFO:tensorflow:Using default config.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/tensor_forest/client/random_forest.py:130: multi_class_head (from tensorflow.contrib.learn.python.learn.estimators.head) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please switch to tf.contrib.estimator.*_head.\n",
      "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpijoguhpz\n",
      "INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f11ef08e518>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_train_distribute': None, '_eval_distribute': None, '_device_fn': None, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1.0\n",
      "}\n",
      ", '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_log_step_count_steps': 100, '_protocol': None, '_session_config': None, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': '/tmp/tmpijoguhpz'}\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:1180: BaseEstimator.__init__ (from tensorflow.contrib.learn.python.learn.estimators.estimator) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please replace uses of any Estimator from tf.contrib.learn with an Estimator from tf.estimator.*\n",
      "WARNING:tensorflow:From /home/huqiu/SPARE/DeepForestOnTF/kfoldwrapper.py:161: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/tensor_forest/client/random_forest.py:130: multi_class_head (from tensorflow.contrib.learn.python.learn.estimators.head) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please switch to tf.contrib.estimator.*_head.\n",
      "\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:428: RunConfig.__init__ (from tensorflow.contrib.learn.python.learn.estimators.run_config) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "When switching to tf.estimator.Estimator, use tf.estimator.RunConfig instead.WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/tensor_forest/client/random_forest.py:130: multi_class_head (from tensorflow.contrib.learn.python.learn.estimators.head) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please switch to tf.contrib.estimator.*_head.\n",
      "\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:1180: BaseEstimator.__init__ (from tensorflow.contrib.learn.python.learn.estimators.estimator) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please replace uses of any Estimator from tf.contrib.learn with an Estimator from tf.estimator.*\n",
      "INFO:tensorflow:Using default config.\n",
      "WARNING:tensorflow:From /home/huqiu/SPARE/DeepForestOnTF/kfoldwrapper.py:161: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmp5cxy35i5\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:428: RunConfig.__init__ (from tensorflow.contrib.learn.python.learn.estimators.run_config) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "When switching to tf.estimator.Estimator, use tf.estimator.RunConfig instead.WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:1180: BaseEstimator.__init__ (from tensorflow.contrib.learn.python.learn.estimators.estimator) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please replace uses of any Estimator from tf.contrib.learn with an Estimator from tf.estimator.*INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f11ec367630>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_train_distribute': None, '_eval_distribute': None, '_device_fn': None, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1.0\n",
      "}\n",
      ", '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_log_step_count_steps': 100, '_protocol': None, '_session_config': None, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': '/tmp/tmp5cxy35i5'}\n",
      "\n",
      "INFO:tensorflow:Using default config.\n",
      "\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/tensor_forest/client/random_forest.py:130: multi_class_head (from tensorflow.contrib.learn.python.learn.estimators.head) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please switch to tf.contrib.estimator.*_head.\n",
      "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmphbdjgny9\n",
      "INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f1210333588>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_train_distribute': None, '_eval_distribute': None, '_device_fn': None, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1.0\n",
      "}\n",
      ", '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_log_step_count_steps': 100, '_protocol': None, '_session_config': None, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': '/tmp/tmphbdjgny9'}WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:509: SKCompat.__init__ (from tensorflow.contrib.learn.python.learn.estimators.estimator) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please switch to the Estimator interface.\n",
      "\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:428: RunConfig.__init__ (from tensorflow.contrib.learn.python.learn.estimators.run_config) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "When switching to tf.estimator.Estimator, use tf.estimator.RunConfig instead.\n",
      "INFO:tensorflow:Using default config.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:1180: BaseEstimator.__init__ (from tensorflow.contrib.learn.python.learn.estimators.estimator) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please replace uses of any Estimator from tf.contrib.learn with an Estimator from tf.estimator.*WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpn3v55j1j\n",
      "INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f11fc8df550>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_train_distribute': None, '_eval_distribute': None, '_device_fn': None, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1.0\n",
      "}\n",
      ", '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_log_step_count_steps': 100, '_protocol': None, '_session_config': None, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': '/tmp/tmpn3v55j1j'}\n",
      "\n",
      "WARNING:tensorflow:From /home/huqiu/SPARE/DeepForestOnTF/kfoldwrapper.py:161: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:428: RunConfig.__init__ (from tensorflow.contrib.learn.python.learn.estimators.run_config) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "When switching to tf.estimator.Estimator, use tf.estimator.RunConfig instead.\n",
      "INFO:tensorflow:Using default config.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:143: setup_train_data_feeder (from tensorflow.contrib.learn.python.learn.learn_io.data_feeder) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tensorflow/transform or tf.data.\n",
      "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpf_cp5r29\n",
      "INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f11dd0ee5c0>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_train_distribute': None, '_eval_distribute': None, '_device_fn': None, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1.0\n",
      "}\n",
      ", '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_log_step_count_steps': 100, '_protocol': None, '_session_config': None, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': '/tmp/tmpf_cp5r29'}\n",
      "WARNING:tensorflow:From /home/huqiu/SPARE/DeepForestOnTF/kfoldwrapper.py:161: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))WARNING:tensorflow:From /home/huqiu/SPARE/DeepForestOnTF/kfoldwrapper.py:161: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "\n",
      "WARNING:tensorflow:From /home/huqiu/SPARE/DeepForestOnTF/kfoldwrapper.py:161: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "WARNING:tensorflow:From /home/huqiu/SPARE/DeepForestOnTF/kfoldwrapper.py:161: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))WARNING:tensorflow:From /home/huqiu/SPARE/DeepForestOnTF/kfoldwrapper.py:161: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:96: extract_dask_data (from tensorflow.contrib.learn.python.learn.learn_io.dask_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please feed input to tf.data to support dask.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:509: SKCompat.__init__ (from tensorflow.contrib.learn.python.learn.estimators.estimator) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please switch to the Estimator interface.WARNING:tensorflow:From /home/huqiu/SPARE/DeepForestOnTF/kfoldwrapper.py:161: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "\n",
      "WARNING:tensorflow:From /home/huqiu/SPARE/DeepForestOnTF/kfoldwrapper.py:161: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/tensor_forest/client/random_forest.py:130: multi_class_head (from tensorflow.contrib.learn.python.learn.estimators.head) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please switch to tf.contrib.estimator.*_head.WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/tensor_forest/client/random_forest.py:130: multi_class_head (from tensorflow.contrib.learn.python.learn.estimators.head) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please switch to tf.contrib.estimator.*_head.\n",
      "\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:509: SKCompat.__init__ (from tensorflow.contrib.learn.python.learn.estimators.estimator) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please switch to the Estimator interface.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:143: setup_train_data_feeder (from tensorflow.contrib.learn.python.learn.learn_io.data_feeder) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tensorflow/transform or tf.data.WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:509: SKCompat.__init__ (from tensorflow.contrib.learn.python.learn.estimators.estimator) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please switch to the Estimator interface.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:98: extract_dask_labels (from tensorflow.contrib.learn.python.learn.learn_io.dask_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please feed input to tf.data to support dask.\n",
      "\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:509: SKCompat.__init__ (from tensorflow.contrib.learn.python.learn.estimators.estimator) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please switch to the Estimator interface.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:143: setup_train_data_feeder (from tensorflow.contrib.learn.python.learn.learn_io.data_feeder) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tensorflow/transform or tf.data.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:1180: BaseEstimator.__init__ (from tensorflow.contrib.learn.python.learn.estimators.estimator) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please replace uses of any Estimator from tf.contrib.learn with an Estimator from tf.estimator.*WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:96: extract_dask_data (from tensorflow.contrib.learn.python.learn.learn_io.dask_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please feed input to tf.data to support dask.WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:1180: BaseEstimator.__init__ (from tensorflow.contrib.learn.python.learn.estimators.estimator) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please replace uses of any Estimator from tf.contrib.learn with an Estimator from tf.estimator.*\n",
      "\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:143: setup_train_data_feeder (from tensorflow.contrib.learn.python.learn.learn_io.data_feeder) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please use tensorflow/transform or tf.data.\n",
      "\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:100: extract_pandas_data (from tensorflow.contrib.learn.python.learn.learn_io.pandas_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please access pandas data directly.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:143: setup_train_data_feeder (from tensorflow.contrib.learn.python.learn.learn_io.data_feeder) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tensorflow/transform or tf.data.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:428: RunConfig.__init__ (from tensorflow.contrib.learn.python.learn.estimators.run_config) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "When switching to tf.estimator.Estimator, use tf.estimator.RunConfig instead.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:96: extract_dask_data (from tensorflow.contrib.learn.python.learn.learn_io.dask_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please feed input to tf.data to support dask.\n",
      "INFO:tensorflow:Using default config.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:428: RunConfig.__init__ (from tensorflow.contrib.learn.python.learn.estimators.run_config) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "When switching to tf.estimator.Estimator, use tf.estimator.RunConfig instead.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:98: extract_dask_labels (from tensorflow.contrib.learn.python.learn.learn_io.dask_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please feed input to tf.data to support dask.WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:96: extract_dask_data (from tensorflow.contrib.learn.python.learn.learn_io.dask_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please feed input to tf.data to support dask.WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpfia_ijqi\n",
      "\n",
      "\n",
      "INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f11dd0c86a0>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_train_distribute': None, '_eval_distribute': None, '_device_fn': None, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1.0\n",
      "}\n",
      ", '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_log_step_count_steps': 100, '_protocol': None, '_session_config': None, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': '/tmp/tmpfia_ijqi'}INFO:tensorflow:Using default config.\n",
      "\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:96: extract_dask_data (from tensorflow.contrib.learn.python.learn.learn_io.dask_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please feed input to tf.data to support dask.WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/tensor_forest/client/random_forest.py:130: multi_class_head (from tensorflow.contrib.learn.python.learn.estimators.head) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please switch to tf.contrib.estimator.*_head.WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:102: extract_pandas_labels (from tensorflow.contrib.learn.python.learn.learn_io.pandas_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please access pandas data directly.\n",
      "\n",
      "\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:98: extract_dask_labels (from tensorflow.contrib.learn.python.learn.learn_io.dask_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please feed input to tf.data to support dask.WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:100: extract_pandas_data (from tensorflow.contrib.learn.python.learn.learn_io.pandas_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please access pandas data directly.\n",
      "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpxkhfnxv_\n",
      "INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f11f44be5f8>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_train_distribute': None, '_eval_distribute': None, '_device_fn': None, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1.0\n",
      "}\n",
      ", '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_log_step_count_steps': 100, '_protocol': None, '_session_config': None, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': '/tmp/tmpxkhfnxv_'}\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:98: extract_dask_labels (from tensorflow.contrib.learn.python.learn.learn_io.dask_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please feed input to tf.data to support dask.\n",
      "\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:159: DataFeeder.__init__ (from tensorflow.contrib.learn.python.learn.learn_io.data_feeder) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tensorflow/transform or tf.data.WARNING:tensorflow:From /home/huqiu/SPARE/DeepForestOnTF/kfoldwrapper.py:161: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:1180: BaseEstimator.__init__ (from tensorflow.contrib.learn.python.learn.estimators.estimator) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please replace uses of any Estimator from tf.contrib.learn with an Estimator from tf.estimator.*WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:98: extract_dask_labels (from tensorflow.contrib.learn.python.learn.learn_io.dask_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please feed input to tf.data to support dask.\n",
      "\n",
      "\n",
      "\n",
      "WARNING:tensorflow:From /home/huqiu/SPARE/DeepForestOnTF/kfoldwrapper.py:161: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:100: extract_pandas_data (from tensorflow.contrib.learn.python.learn.learn_io.pandas_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please access pandas data directly.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:100: extract_pandas_data (from tensorflow.contrib.learn.python.learn.learn_io.pandas_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please access pandas data directly.WARNING:tensorflow:From /home/huqiu/SPARE/DeepForestOnTF/kfoldwrapper.py:161: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:100: extract_pandas_data (from tensorflow.contrib.learn.python.learn.learn_io.pandas_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please access pandas data directly.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:340: check_array (from tensorflow.contrib.learn.python.learn.learn_io.data_feeder) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please convert numpy dtypes explicitly.\n",
      "\n",
      "\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:102: extract_pandas_labels (from tensorflow.contrib.learn.python.learn.learn_io.pandas_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please access pandas data directly.\n",
      "\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:428: RunConfig.__init__ (from tensorflow.contrib.learn.python.learn.estimators.run_config) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "When switching to tf.estimator.Estimator, use tf.estimator.RunConfig instead.\n",
      "INFO:tensorflow:Using default config.\n",
      "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpu4b5e6fb\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:509: SKCompat.__init__ (from tensorflow.contrib.learn.python.learn.estimators.estimator) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please switch to the Estimator interface.WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:159: DataFeeder.__init__ (from tensorflow.contrib.learn.python.learn.learn_io.data_feeder) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tensorflow/transform or tf.data.WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:102: extract_pandas_labels (from tensorflow.contrib.learn.python.learn.learn_io.pandas_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please access pandas data directly.INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f11dd0bb668>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_train_distribute': None, '_eval_distribute': None, '_device_fn': None, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1.0\n",
      "}\n",
      ", '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_log_step_count_steps': 100, '_protocol': None, '_session_config': None, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': '/tmp/tmpu4b5e6fb'}WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:102: extract_pandas_labels (from tensorflow.contrib.learn.python.learn.learn_io.pandas_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please access pandas data directly.\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 817449704, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0}\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:102: extract_pandas_labels (from tensorflow.contrib.learn.python.learn.learn_io.pandas_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please access pandas data directly.WARNING:tensorflow:From /home/huqiu/SPARE/DeepForestOnTF/kfoldwrapper.py:161: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:159: DataFeeder.__init__ (from tensorflow.contrib.learn.python.learn.learn_io.data_feeder) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tensorflow/transform or tf.data.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:159: DataFeeder.__init__ (from tensorflow.contrib.learn.python.learn.learn_io.data_feeder) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tensorflow/transform or tf.data.WARNING:tensorflow:From /home/huqiu/SPARE/DeepForestOnTF/kfoldwrapper.py:161: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:340: check_array (from tensorflow.contrib.learn.python.learn.learn_io.data_feeder) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please convert numpy dtypes explicitly.WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:143: setup_train_data_feeder (from tensorflow.contrib.learn.python.learn.learn_io.data_feeder) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tensorflow/transform or tf.data.\n",
      "\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:159: DataFeeder.__init__ (from tensorflow.contrib.learn.python.learn.learn_io.data_feeder) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tensorflow/transform or tf.data.\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 258907521, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0}\n",
      "\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:340: check_array (from tensorflow.contrib.learn.python.learn.learn_io.data_feeder) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please convert numpy dtypes explicitly.WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:509: SKCompat.__init__ (from tensorflow.contrib.learn.python.learn.estimators.estimator) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please switch to the Estimator interface.\n",
      "\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:96: extract_dask_data (from tensorflow.contrib.learn.python.learn.learn_io.dask_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please feed input to tf.data to support dask.\n",
      "WARNING:tensorflow:From /home/huqiu/SPARE/DeepForestOnTF/kfoldwrapper.py:161: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:340: check_array (from tensorflow.contrib.learn.python.learn.learn_io.data_feeder) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please convert numpy dtypes explicitly.\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:340: check_array (from tensorflow.contrib.learn.python.learn.learn_io.data_feeder) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please convert numpy dtypes explicitly.\n",
      "\n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 67480226, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0}\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:143: setup_train_data_feeder (from tensorflow.contrib.learn.python.learn.learn_io.data_feeder) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tensorflow/transform or tf.data.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:98: extract_dask_labels (from tensorflow.contrib.learn.python.learn.learn_io.dask_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please feed input to tf.data to support dask.\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:509: SKCompat.__init__ (from tensorflow.contrib.learn.python.learn.estimators.estimator) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please switch to the Estimator interface.\n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 946637773, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0}\n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 447878714, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0}\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:96: extract_dask_data (from tensorflow.contrib.learn.python.learn.learn_io.dask_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please feed input to tf.data to support dask.WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:143: setup_train_data_feeder (from tensorflow.contrib.learn.python.learn.learn_io.data_feeder) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tensorflow/transform or tf.data.\n",
      "\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:100: extract_pandas_data (from tensorflow.contrib.learn.python.learn.learn_io.pandas_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please access pandas data directly.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:96: extract_dask_data (from tensorflow.contrib.learn.python.learn.learn_io.dask_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please feed input to tf.data to support dask.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:98: extract_dask_labels (from tensorflow.contrib.learn.python.learn.learn_io.dask_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please feed input to tf.data to support dask.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:102: extract_pandas_labels (from tensorflow.contrib.learn.python.learn.learn_io.pandas_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please access pandas data directly.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:98: extract_dask_labels (from tensorflow.contrib.learn.python.learn.learn_io.dask_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please feed input to tf.data to support dask.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:100: extract_pandas_data (from tensorflow.contrib.learn.python.learn.learn_io.pandas_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please access pandas data directly.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:100: extract_pandas_data (from tensorflow.contrib.learn.python.learn.learn_io.pandas_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please access pandas data directly.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:159: DataFeeder.__init__ (from tensorflow.contrib.learn.python.learn.learn_io.data_feeder) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tensorflow/transform or tf.data.WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:102: extract_pandas_labels (from tensorflow.contrib.learn.python.learn.learn_io.pandas_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please access pandas data directly.\n",
      "\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:102: extract_pandas_labels (from tensorflow.contrib.learn.python.learn.learn_io.pandas_io) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please access pandas data directly.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:340: check_array (from tensorflow.contrib.learn.python.learn.learn_io.data_feeder) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please convert numpy dtypes explicitly.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:159: DataFeeder.__init__ (from tensorflow.contrib.learn.python.learn.learn_io.data_feeder) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tensorflow/transform or tf.data.WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:159: DataFeeder.__init__ (from tensorflow.contrib.learn.python.learn.learn_io.data_feeder) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tensorflow/transform or tf.data.\n",
      "\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 919198790, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0}\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:340: check_array (from tensorflow.contrib.learn.python.learn.learn_io.data_feeder) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please convert numpy dtypes explicitly.WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:340: check_array (from tensorflow.contrib.learn.python.learn.learn_io.data_feeder) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please convert numpy dtypes explicitly.\n",
      "\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 140551837, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0}\n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 269817086, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:678: ModelFnOps.__new__ (from tensorflow.contrib.learn.python.learn.estimators.model_fn) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "When switching to tf.estimator.Estimator, use tf.estimator.EstimatorSpec. You can use the `estimator_spec` method to create an equivalent one.WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:678: ModelFnOps.__new__ (from tensorflow.contrib.learn.python.learn.estimators.model_fn) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "When switching to tf.estimator.Estimator, use tf.estimator.EstimatorSpec. You can use the `estimator_spec` method to create an equivalent one.\n",
      "\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:678: ModelFnOps.__new__ (from tensorflow.contrib.learn.python.learn.estimators.model_fn) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "When switching to tf.estimator.Estimator, use tf.estimator.EstimatorSpec. You can use the `estimator_spec` method to create an equivalent one.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:678: ModelFnOps.__new__ (from tensorflow.contrib.learn.python.learn.estimators.model_fn) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "When switching to tf.estimator.Estimator, use tf.estimator.EstimatorSpec. You can use the `estimator_spec` method to create an equivalent one.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:678: ModelFnOps.__new__ (from tensorflow.contrib.learn.python.learn.estimators.model_fn) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "When switching to tf.estimator.Estimator, use tf.estimator.EstimatorSpec. You can use the `estimator_spec` method to create an equivalent one.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:678: ModelFnOps.__new__ (from tensorflow.contrib.learn.python.learn.estimators.model_fn) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "When switching to tf.estimator.Estimator, use tf.estimator.EstimatorSpec. You can use the `estimator_spec` method to create an equivalent one.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:678: ModelFnOps.__new__ (from tensorflow.contrib.learn.python.learn.estimators.model_fn) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "When switching to tf.estimator.Estimator, use tf.estimator.EstimatorSpec. You can use the `estimator_spec` method to create an equivalent one.WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:From /home/huqiu/programs/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:678: ModelFnOps.__new__ (from tensorflow.contrib.learn.python.learn.estimators.model_fn) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "When switching to tf.estimator.Estimator, use tf.estimator.EstimatorSpec. You can use the `estimator_spec` method to create an equivalent one.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmp5cxy35i5/model.ckpt.\n",
      "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpf_cp5r29/model.ckpt.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpijoguhpz/model.ckpt.\n",
      "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmphbdjgny9/model.ckpt.\n",
      "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpn3v55j1j/model.ckpt.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpxkhfnxv_/model.ckpt.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "\n",
      "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpfia_ijqi/model.ckpt.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpu4b5e6fb/model.ckpt.\n",
      "\n",
      "INFO:tensorflow:loss = 3.258082, step = 1\n",
      "INFO:tensorflow:TensorForestLossHook resetting last_step.\n",
      "INFO:tensorflow:loss = 3.258082, step = 1\n",
      "INFO:tensorflow:TensorForestLossHook resetting last_step.\n",
      "INFO:tensorflow:loss = 3.258082, step = 1\n",
      "INFO:tensorflow:TensorForestLossHook resetting last_step.\n",
      "INFO:tensorflow:loss = 3.258082, step = 1\n",
      "INFO:tensorflow:TensorForestLossHook resetting last_step.\n",
      "INFO:tensorflow:loss = 3.258082, step = 1\n",
      "INFO:tensorflow:TensorForestLossHook resetting last_step.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:loss = 3.258082, step = 1\n",
      "INFO:tensorflow:TensorForestLossHook resetting last_step.\n",
      "INFO:tensorflow:loss = 3.258082, step = 1\n",
      "INFO:tensorflow:TensorForestLossHook resetting last_step.\n",
      "INFO:tensorflow:loss = 3.258082, step = 1\n",
      "INFO:tensorflow:TensorForestLossHook resetting last_step.\n",
      "INFO:tensorflow:global_step/sec: 0.4056\n",
      "INFO:tensorflow:global_step/sec: 0.406904\n",
      "INFO:tensorflow:loss = 2.6392636, step = 101 (247.160 sec)\n",
      "INFO:tensorflow:loss = 2.6445897, step = 101 (246.791 sec)\n",
      "INFO:tensorflow:global_step/sec: 0.400662\n",
      "INFO:tensorflow:loss = 2.640541, step = 101 (249.666 sec)\n",
      "INFO:tensorflow:global_step/sec: 0.399522\n",
      "INFO:tensorflow:loss = 2.6429946, step = 101 (250.345 sec)\n",
      "INFO:tensorflow:global_step/sec: 0.395594\n",
      "INFO:tensorflow:loss = 2.640856, step = 101 (252.871 sec)\n",
      "INFO:tensorflow:global_step/sec: 0.390293\n",
      "INFO:tensorflow:loss = 2.6435103, step = 101 (256.245 sec)\n",
      "INFO:tensorflow:global_step/sec: 0.383695\n",
      "INFO:tensorflow:loss = 2.6402009, step = 101 (260.753 sec)\n",
      "INFO:tensorflow:global_step/sec: 0.382244\n",
      "INFO:tensorflow:loss = 2.6399357, step = 101 (261.638 sec)\n",
      "INFO:tensorflow:TensorForestLossHook requesting stop.\n",
      "INFO:tensorflow:Saving checkpoints for 127 into /tmp/tmphbdjgny9/model.ckpt.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:TensorForestLossHook requesting stop.\n",
      "INFO:tensorflow:Saving checkpoints for 127 into /tmp/tmp5cxy35i5/model.ckpt.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:9: clean up resources: None\n",
      "INFO:tensorflow:Loss for final step: 2.6445909.\n",
      "WARNING:tensorflow:From /home/huqiu/SPARE/DeepForestOnTF/kfoldwrapper.py:167: calling BaseEstimator.predict (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 946637773, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "INFO:tensorflow:9: clean up resources: None\n",
      "INFO:tensorflow:TensorForestLossHook requesting stop.\n",
      "INFO:tensorflow:Saving checkpoints for 129 into /tmp/tmpf_cp5r29/model.ckpt.\n",
      "INFO:tensorflow:Loss for final step: 2.6392643.\n",
      "INFO:tensorflow:TensorForestLossHook requesting stop.\n",
      "INFO:tensorflow:Saving checkpoints for 128 into /tmp/tmpijoguhpz/model.ckpt.\n",
      "WARNING:tensorflow:From /home/huqiu/SPARE/DeepForestOnTF/kfoldwrapper.py:167: calling BaseEstimator.predict (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 258907521, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:TensorForestLossHook requesting stop.\n",
      "INFO:tensorflow:Saving checkpoints for 128 into /tmp/tmpn3v55j1j/model.ckpt.\n",
      "INFO:tensorflow:TensorForestLossHook requesting stop.\n",
      "INFO:tensorflow:Saving checkpoints for 127 into /tmp/tmpfia_ijqi/model.ckpt.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:9: clean up resources: None\n",
      "INFO:tensorflow:Loss for final step: 2.6405418.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'WARNING:tensorflow:From /home/huqiu/SPARE/DeepForestOnTF/kfoldwrapper.py:167: calling BaseEstimator.predict (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 67480226, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:9: clean up resources: None\n",
      "INFO:tensorflow:Loss for final step: 2.6408565.\n",
      "WARNING:tensorflow:From /home/huqiu/SPARE/DeepForestOnTF/kfoldwrapper.py:167: calling BaseEstimator.predict (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 817449704, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "INFO:tensorflow:9: clean up resources: None\n",
      "INFO:tensorflow:Loss for final step: 2.643511.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:9: clean up resources: None\n",
      "INFO:tensorflow:Loss for final step: 2.6429958.\n",
      "WARNING:tensorflow:From /home/huqiu/SPARE/DeepForestOnTF/kfoldwrapper.py:167: calling BaseEstimator.predict (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "WARNING:tensorflow:From /home/huqiu/SPARE/DeepForestOnTF/kfoldwrapper.py:167: calling BaseEstimator.predict (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 919198790, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 447878714, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "INFO:tensorflow:TensorForestLossHook requesting stop.\n",
      "INFO:tensorflow:Saving checkpoints for 127 into /tmp/tmpxkhfnxv_/model.ckpt.\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:TensorForestLossHook requesting stop.\n",
      "INFO:tensorflow:Saving checkpoints for 128 into /tmp/tmpu4b5e6fb/model.ckpt.\n",
      "INFO:tensorflow:9: clean up resources: None\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:Loss for final step: 2.6402018.\n",
      "WARNING:tensorflow:From /home/huqiu/SPARE/DeepForestOnTF/kfoldwrapper.py:167: calling BaseEstimator.predict (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 140551837, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:9: clean up resources: None\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:Loss for final step: 2.6399367.\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "WARNING:tensorflow:From /home/huqiu/SPARE/DeepForestOnTF/kfoldwrapper.py:167: calling BaseEstimator.predict (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 269817086, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmphbdjgny9/model.ckpt-127\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpf_cp5r29/model.ckpt-129\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmp5cxy35i5/model.ckpt-127\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpijoguhpz/model.ckpt-128\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpn3v55j1j/model.ckpt-128\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpfia_ijqi/model.ckpt-127\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpxkhfnxv_/model.ckpt-127\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "Accuracy(layer-0-estimator-7-3folds - train_0) = 90.7882%\n",
      "Accuracy(layer-0-estimator-1-3folds - train_0) = 90.8631%\n",
      "Accuracy(layer-0-estimator-5-3folds - train_0) = 90.9755%\n",
      "Accuracy(layer-0-estimator-3-3folds - train_0) = 90.6572%\n",
      "Accuracy(layer-0-estimator-0-3folds - train_0) = 90.5823%\n",
      "Accuracy(layer-0-estimator-2-3folds - train_0) = 91.3125%\n",
      "Accuracy(layer-0-estimator-4-3folds - train_0) = 90.1517%\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 919198790, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 140551837, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 447878714, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 817449704, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 258907521, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 67480226, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "\n",
      "\n",
      "\n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 946637773, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpu4b5e6fb/model.ckpt-128\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "Accuracy(layer-0-estimator-6-3folds - train_0) = 90.4512%\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 269817086, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpfia_ijqi/model.ckpt-127\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmp5cxy35i5/model.ckpt-127\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpxkhfnxv_/model.ckpt-127\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmphbdjgny9/model.ckpt-127\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpn3v55j1j/model.ckpt-128\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpf_cp5r29/model.ckpt-129\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpijoguhpz/model.ckpt-128\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Using default config.\n",
      "INFO:tensorflow:Using default config.\n",
      "INFO:tensorflow:Using default config.\n",
      "INFO:tensorflow:Using default config.\n",
      "INFO:tensorflow:Using default config.\n",
      "INFO:tensorflow:Using default config.\n",
      "INFO:tensorflow:Using default config.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpu4b5e6fb/model.ckpt-128\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpmqksqaqs\n",
      "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpfzia16t2\n",
      "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpy3ddh_cw\n",
      "INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f11db419fd0>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_train_distribute': None, '_eval_distribute': None, '_device_fn': None, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1.0\n",
      "}\n",
      ", '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_log_step_count_steps': 100, '_protocol': None, '_session_config': None, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': '/tmp/tmpmqksqaqs'}INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f11fc86f2b0>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_train_distribute': None, '_eval_distribute': None, '_device_fn': None, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1.0\n",
      "}\n",
      ", '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_log_step_count_steps': 100, '_protocol': None, '_session_config': None, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': '/tmp/tmpfzia16t2'}INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f11afa06828>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_train_distribute': None, '_eval_distribute': None, '_device_fn': None, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1.0\n",
      "}\n",
      ", '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_log_step_count_steps': 100, '_protocol': None, '_session_config': None, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': '/tmp/tmpy3ddh_cw'}WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpl_cn_2qo\n",
      "\n",
      "\n",
      "\n",
      "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpdusyzrki\n",
      "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpdolc071s\n",
      "INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f11ec111240>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_train_distribute': None, '_eval_distribute': None, '_device_fn': None, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1.0\n",
      "}\n",
      ", '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_log_step_count_steps': 100, '_protocol': None, '_session_config': None, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': '/tmp/tmpl_cn_2qo'}INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f12f00f81d0>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_train_distribute': None, '_eval_distribute': None, '_device_fn': None, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1.0\n",
      "}\n",
      ", '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_log_step_count_steps': 100, '_protocol': None, '_session_config': None, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': '/tmp/tmpdusyzrki'}\n",
      "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpjmqpyq06\n",
      "INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f11d356e6d8>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_train_distribute': None, '_eval_distribute': None, '_device_fn': None, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1.0\n",
      "}\n",
      ", '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_log_step_count_steps': 100, '_protocol': None, '_session_config': None, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': '/tmp/tmpjmqpyq06'}\n",
      "\n",
      "INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f11f444f4e0>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_train_distribute': None, '_eval_distribute': None, '_device_fn': None, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1.0\n",
      "}\n",
      ", '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_log_step_count_steps': 100, '_protocol': None, '_session_config': None, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': '/tmp/tmpdolc071s'}\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 447878714, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0}\n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 946637773, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 140551837, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0}\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 919198790, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0}\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 817449704, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0}\n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 67480226, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0}\n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 258907521, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0}\n",
      "INFO:tensorflow:Using default config.\n",
      "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpfh5ukcu5\n",
      "INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f11db40cf98>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_train_distribute': None, '_eval_distribute': None, '_device_fn': None, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1.0\n",
      "}\n",
      ", '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_log_step_count_steps': 100, '_protocol': None, '_session_config': None, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': '/tmp/tmpfh5ukcu5'}\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 269817086, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0}\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpmqksqaqs/model.ckpt.\n",
      "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpl_cn_2qo/model.ckpt.\n",
      "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpfzia16t2/model.ckpt.\n",
      "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpy3ddh_cw/model.ckpt.\n",
      "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpdusyzrki/model.ckpt.\n",
      "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpdolc071s/model.ckpt.\n",
      "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpjmqpyq06/model.ckpt.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpfh5ukcu5/model.ckpt.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:loss = 3.2580822, step = 1\n",
      "INFO:tensorflow:loss = 3.2580822, step = 1\n",
      "INFO:tensorflow:loss = 3.2580822, step = 1\n",
      "INFO:tensorflow:loss = 3.2580822, step = 1\n",
      "INFO:tensorflow:TensorForestLossHook resetting last_step.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:TensorForestLossHook resetting last_step.\n",
      "INFO:tensorflow:loss = 3.2580822, step = 1\n",
      "INFO:tensorflow:TensorForestLossHook resetting last_step.\n",
      "INFO:tensorflow:TensorForestLossHook resetting last_step.\n",
      "INFO:tensorflow:loss = 3.2580822, step = 1\n",
      "INFO:tensorflow:TensorForestLossHook resetting last_step.\n",
      "INFO:tensorflow:TensorForestLossHook resetting last_step.\n",
      "INFO:tensorflow:loss = 3.2580822, step = 1\n",
      "INFO:tensorflow:TensorForestLossHook resetting last_step.\n",
      "INFO:tensorflow:loss = 3.2580822, step = 1\n",
      "INFO:tensorflow:TensorForestLossHook resetting last_step.\n",
      "INFO:tensorflow:global_step/sec: 0.440036\n",
      "INFO:tensorflow:loss = 2.6410074, step = 101 (227.334 sec)\n",
      "INFO:tensorflow:global_step/sec: 0.433504\n",
      "INFO:tensorflow:loss = 2.6411898, step = 101 (230.681 sec)\n",
      "INFO:tensorflow:global_step/sec: 0.426241\n",
      "INFO:tensorflow:loss = 2.6369135, step = 101 (234.639 sec)\n",
      "INFO:tensorflow:global_step/sec: 0.425146\n",
      "INFO:tensorflow:loss = 2.6420333, step = 101 (235.231 sec)\n",
      "INFO:tensorflow:global_step/sec: 0.424461\n",
      "INFO:tensorflow:loss = 2.6349022, step = 101 (235.553 sec)\n",
      "INFO:tensorflow:global_step/sec: 0.423868\n",
      "INFO:tensorflow:loss = 2.6387684, step = 101 (235.939 sec)\n",
      "INFO:tensorflow:global_step/sec: 0.4242\n",
      "INFO:tensorflow:loss = 2.6379914, step = 101 (235.791 sec)\n",
      "INFO:tensorflow:global_step/sec: 0.419995\n",
      "INFO:tensorflow:loss = 2.6397412, step = 101 (238.137 sec)\n",
      "INFO:tensorflow:TensorForestLossHook requesting stop.\n",
      "INFO:tensorflow:Saving checkpoints for 129 into /tmp/tmpdolc071s/model.ckpt.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:TensorForestLossHook requesting stop.\n",
      "INFO:tensorflow:Saving checkpoints for 127 into /tmp/tmpdusyzrki/model.ckpt.\n",
      "INFO:tensorflow:TensorForestLossHook requesting stop.\n",
      "INFO:tensorflow:Saving checkpoints for 129 into /tmp/tmpl_cn_2qo/model.ckpt.\n",
      "INFO:tensorflow:TensorForestLossHook requesting stop.\n",
      "INFO:tensorflow:Saving checkpoints for 127 into /tmp/tmpfh5ukcu5/model.ckpt.\n",
      "INFO:tensorflow:9: clean up resources: None\n",
      "INFO:tensorflow:Loss for final step: 2.6410096.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 140551837, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:TensorForestLossHook requesting stop.\n",
      "INFO:tensorflow:Saving checkpoints for 129 into /tmp/tmpjmqpyq06/model.ckpt.\n",
      "INFO:tensorflow:9: clean up resources: None\n",
      "INFO:tensorflow:Loss for final step: 2.6387694.\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:9: clean up resources: None\n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 67480226, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}INFO:tensorflow:9: clean up resources: None\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "\n",
      "INFO:tensorflow:Loss for final step: 2.6411915.\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:TensorForestLossHook requesting stop.\n",
      "INFO:tensorflow:Saving checkpoints for 129 into /tmp/tmpy3ddh_cw/model.ckpt.\n",
      "INFO:tensorflow:Loss for final step: 2.6379929.\n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 258907521, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 269817086, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:TensorForestLossHook requesting stop.\n",
      "INFO:tensorflow:Saving checkpoints for 129 into /tmp/tmpfzia16t2/model.ckpt.\n",
      "INFO:tensorflow:9: clean up resources: None\n",
      "INFO:tensorflow:Loss for final step: 2.6369147.\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 817449704, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "\n",
      "INFO:tensorflow:9: clean up resources: None\n",
      "INFO:tensorflow:Loss for final step: 2.6420348.\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 946637773, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "INFO:tensorflow:9: clean up resources: None\n",
      "INFO:tensorflow:Loss for final step: 2.634903.\n",
      "INFO:tensorflow:TensorForestLossHook requesting stop.\n",
      "INFO:tensorflow:Saving checkpoints for 129 into /tmp/tmpmqksqaqs/model.ckpt.\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 447878714, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:9: clean up resources: None\n",
      "INFO:tensorflow:Loss for final step: 2.6397424.\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 919198790, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpfh5ukcu5/model.ckpt-127\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpdolc071s/model.ckpt-129\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpdusyzrki/model.ckpt-127\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpjmqpyq06/model.ckpt-129\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpl_cn_2qo/model.ckpt-129\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpy3ddh_cw/model.ckpt-129\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpfzia16t2/model.ckpt-129\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "Accuracy(layer-0-estimator-4-3folds - train_1) = 90.9074%\n",
      "Accuracy(layer-0-estimator-0-3folds - train_1) = 90.7199%\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 140551837, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "Accuracy(layer-0-estimator-3-3folds - train_1) = 90.4012%\n",
      "Accuracy(layer-0-estimator-5-3folds - train_1) = 90.6449%\n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 817449704, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}INFO:tensorflow:Constructing forest with params = \n",
      "\n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 67480226, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "Accuracy(layer-0-estimator-2-3folds - train_1) = 90.4762%\n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 258907521, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "Accuracy(layer-0-estimator-6-3folds - train_1) = 91.0011%\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 946637773, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 269817086, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "Accuracy(layer-0-estimator-1-3folds - train_1) = 90.4574%\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 447878714, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}INFO:tensorflow:Graph was finalized.\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpmqksqaqs/model.ckpt-129\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "Accuracy(layer-0-estimator-7-3folds - train_1) = 90.4762%\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 919198790, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpdolc071s/model.ckpt-129\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpjmqpyq06/model.ckpt-129\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpl_cn_2qo/model.ckpt-129\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpy3ddh_cw/model.ckpt-129\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpdusyzrki/model.ckpt-127\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpfh5ukcu5/model.ckpt-127\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpfzia16t2/model.ckpt-129\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Using default config.\n",
      "INFO:tensorflow:Using default config.\n",
      "INFO:tensorflow:Using default config.\n",
      "INFO:tensorflow:Using default config.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpmqksqaqs/model.ckpt-129\n",
      "INFO:tensorflow:Using default config.\n",
      "INFO:tensorflow:Using default config.\n",
      "INFO:tensorflow:Using default config.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Using default config.\n",
      "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpbpcp9pzp\n",
      "INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f11d0414160>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_train_distribute': None, '_eval_distribute': None, '_device_fn': None, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1.0\n",
      "}\n",
      ", '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_log_step_count_steps': 100, '_protocol': None, '_session_config': None, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': '/tmp/tmpbpcp9pzp'}\n",
      "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmph0vmjxou\n",
      "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpf6qwua0x\n",
      "INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f11eccbcdd8>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_train_distribute': None, '_eval_distribute': None, '_device_fn': None, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1.0\n",
      "}\n",
      ", '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_log_step_count_steps': 100, '_protocol': None, '_session_config': None, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': '/tmp/tmph0vmjxou'}WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmp7g1v4ic7\n",
      "\n",
      "INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f11e4207cf8>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_train_distribute': None, '_eval_distribute': None, '_device_fn': None, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1.0\n",
      "}\n",
      ", '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_log_step_count_steps': 100, '_protocol': None, '_session_config': None, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': '/tmp/tmpf6qwua0x'}WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpjwskjg3k\n",
      "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpvw0jmvf9\n",
      "INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f11dccdaac8>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_train_distribute': None, '_eval_distribute': None, '_device_fn': None, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1.0\n",
      "}\n",
      ", '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_log_step_count_steps': 100, '_protocol': None, '_session_config': None, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': '/tmp/tmp7g1v4ic7'}\n",
      "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmprqhgeip1\n",
      "\n",
      "INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f11d1550cc0>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_train_distribute': None, '_eval_distribute': None, '_device_fn': None, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1.0\n",
      "}\n",
      ", '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_log_step_count_steps': 100, '_protocol': None, '_session_config': None, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': '/tmp/tmprqhgeip1'}INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f11dfc54d68>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_train_distribute': None, '_eval_distribute': None, '_device_fn': None, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1.0\n",
      "}\n",
      ", '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_log_step_count_steps': 100, '_protocol': None, '_session_config': None, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': '/tmp/tmpvw0jmvf9'}\n",
      "INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f11d5eb1e10>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_train_distribute': None, '_eval_distribute': None, '_device_fn': None, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1.0\n",
      "}\n",
      ", '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_log_step_count_steps': 100, '_protocol': None, '_session_config': None, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': '/tmp/tmpjwskjg3k'}WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmphe0viouo\n",
      "\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "\n",
      "INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f11da696a90>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_train_distribute': None, '_eval_distribute': None, '_device_fn': None, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1.0\n",
      "}\n",
      ", '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_log_step_count_steps': 100, '_protocol': None, '_session_config': None, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': '/tmp/tmphe0viouo'}INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 67480226, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Constructing forest with params = \n",
      "\n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 258907521, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0}\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 269817086, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0}\n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 140551837, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0}\n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 817449704, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0}\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 919198790, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0}\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 447878714, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0}\n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 946637773, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0}\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'INFO:tensorflow:Create CheckpointSaverHook.\n",
      "\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'INFO:tensorflow:Graph was finalized.\n",
      "\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "\n",
      "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpjwskjg3k/model.ckpt.\n",
      "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmp7g1v4ic7/model.ckpt.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpbpcp9pzp/model.ckpt.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpf6qwua0x/model.ckpt.\n",
      "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmph0vmjxou/model.ckpt.\n",
      "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmphe0viouo/model.ckpt.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmprqhgeip1/model.ckpt.\n",
      "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpvw0jmvf9/model.ckpt.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:loss = 3.2580822, step = 1\n",
      "INFO:tensorflow:TensorForestLossHook resetting last_step.\n",
      "INFO:tensorflow:loss = 3.2580822, step = 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:TensorForestLossHook resetting last_step.\n",
      "INFO:tensorflow:loss = 3.2580822, step = 1\n",
      "INFO:tensorflow:TensorForestLossHook resetting last_step.\n",
      "INFO:tensorflow:loss = 3.2580822, step = 1\n",
      "INFO:tensorflow:TensorForestLossHook resetting last_step.\n",
      "INFO:tensorflow:loss = 3.2580822, step = 1\n",
      "INFO:tensorflow:TensorForestLossHook resetting last_step.\n",
      "INFO:tensorflow:loss = 3.2580822, step = 1\n",
      "INFO:tensorflow:loss = 3.2580822, step = 1\n",
      "INFO:tensorflow:TensorForestLossHook resetting last_step.\n",
      "INFO:tensorflow:TensorForestLossHook resetting last_step.\n",
      "INFO:tensorflow:loss = 3.2580822, step = 1\n",
      "INFO:tensorflow:TensorForestLossHook resetting last_step.\n",
      "INFO:tensorflow:global_step/sec: 0.48341\n",
      "INFO:tensorflow:loss = 2.6423056, step = 101 (206.972 sec)\n",
      "INFO:tensorflow:global_step/sec: 0.480826\n",
      "INFO:tensorflow:loss = 2.6425033, step = 101 (208.018 sec)\n",
      "INFO:tensorflow:global_step/sec: 0.474881\n",
      "INFO:tensorflow:loss = 2.6411302, step = 101 (210.661 sec)\n",
      "INFO:tensorflow:global_step/sec: 0.472901\n",
      "INFO:tensorflow:loss = 2.6411216, step = 101 (211.480 sec)\n",
      "INFO:tensorflow:global_step/sec: 0.474039\n",
      "INFO:tensorflow:loss = 2.6411157, step = 101 (211.071 sec)\n",
      "INFO:tensorflow:global_step/sec: 0.468431\n",
      "INFO:tensorflow:loss = 2.6415071, step = 101 (213.529 sec)\n",
      "INFO:tensorflow:global_step/sec: 0.468425\n",
      "INFO:tensorflow:loss = 2.6366608, step = 101 (213.503 sec)\n",
      "INFO:tensorflow:global_step/sec: 0.462979\n",
      "INFO:tensorflow:loss = 2.6414897, step = 101 (216.411 sec)\n",
      "INFO:tensorflow:TensorForestLossHook requesting stop.\n",
      "INFO:tensorflow:Saving checkpoints for 127 into /tmp/tmp7g1v4ic7/model.ckpt.\n",
      "INFO:tensorflow:TensorForestLossHook requesting stop.\n",
      "INFO:tensorflow:Saving checkpoints for 127 into /tmp/tmphe0viouo/model.ckpt.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:TensorForestLossHook requesting stop.\n",
      "INFO:tensorflow:9: clean up resources: None\n",
      "INFO:tensorflow:9: clean up resources: None\n",
      "INFO:tensorflow:Loss for final step: 2.642503.\n",
      "INFO:tensorflow:Loss for final step: 2.6423068.\n",
      "INFO:tensorflow:TensorForestLossHook requesting stop.\n",
      "INFO:tensorflow:Saving checkpoints for 127 into /tmp/tmpvw0jmvf9/model.ckpt.\n",
      "INFO:tensorflow:Saving checkpoints for 127 into /tmp/tmpbpcp9pzp/model.ckpt.\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 919198790, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 269817086, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "\n",
      "INFO:tensorflow:TensorForestLossHook requesting stop.\n",
      "INFO:tensorflow:Saving checkpoints for 127 into /tmp/tmpjwskjg3k/model.ckpt.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:TensorForestLossHook requesting stop.\n",
      "INFO:tensorflow:Saving checkpoints for 128 into /tmp/tmph0vmjxou/model.ckpt.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:TensorForestLossHook requesting stop.\n",
      "INFO:tensorflow:9: clean up resources: None\n",
      "INFO:tensorflow:Saving checkpoints for 127 into /tmp/tmpf6qwua0x/model.ckpt.\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'\n",
      "INFO:tensorflow:Loss for final step: 2.641116.\n",
      "INFO:tensorflow:9: clean up resources: None\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 946637773, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}INFO:tensorflow:Loss for final step: 2.6411307.\n",
      "\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 67480226, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'INFO:tensorflow:9: clean up resources: None\n",
      "\n",
      "INFO:tensorflow:Loss for final step: 2.641507.\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 447878714, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "INFO:tensorflow:9: clean up resources: None\n",
      "INFO:tensorflow:Loss for final step: 2.6411219.\n",
      "INFO:tensorflow:TensorForestLossHook requesting stop.\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:Saving checkpoints for 127 into /tmp/tmprqhgeip1/model.ckpt.\n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 817449704, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}INFO:tensorflow:9: clean up resources: None\n",
      "\n",
      "INFO:tensorflow:Loss for final step: 2.6366618.\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 258907521, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "WARNING:tensorflow:Issue encountered when serializing resources.\n",
      "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
      "'_Resource' object has no attribute 'name'INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "\n",
      "INFO:tensorflow:9: clean up resources: None\n",
      "INFO:tensorflow:Loss for final step: 2.6414902.\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 140551837, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmphe0viouo/model.ckpt-127\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmp7g1v4ic7/model.ckpt-127\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpbpcp9pzp/model.ckpt-127\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpvw0jmvf9/model.ckpt-127\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpjwskjg3k/model.ckpt-127\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpf6qwua0x/model.ckpt-127\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "Accuracy(layer-0-estimator-7-3folds - train_2) = 90.0282%\n",
      "Accuracy(layer-0-estimator-6-3folds - train_2) = 90.8357%\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmph0vmjxou/model.ckpt-128\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 269817086, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}INFO:tensorflow:Constructing forest with params = \n",
      "\n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 919198790, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "Accuracy(layer-0-estimator-3-3folds - train_2) = 90.7042%\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 67480226, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "Accuracy(layer-0-estimator-2-3folds - train_2) = 90.4977%\n",
      "Accuracy(layer-0-estimator-1-3folds - train_2) = 90.6667%\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 946637773, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 447878714, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmprqhgeip1/model.ckpt-127\n",
      "Accuracy(layer-0-estimator-5-3folds - train_2) = 89.6901%\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 258907521, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "Accuracy(layer-0-estimator-0-3folds - train_2) = 90.8732%\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 817449704, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "Accuracy(layer-0-estimator-4-3folds - train_2) = 91.6244%\n",
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'num_classes': 26, 'num_features': 16, 'regression': False, 'base_random_seed': 140551837, 'bagged_num_features': 16, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 27, 'leaf_model_type': 0, 'stats_model_type': 0, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0, 'params_proto': pruning_type {\n",
      "  prune_every_samples {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "finish_type {\n",
      "  check_every_steps {\n",
      "    constant_value: 0.0\n",
      "  }\n",
      "}\n",
      "num_trees: 50\n",
      "max_nodes: 1000\n",
      "num_outputs: 26\n",
      "num_splits_to_consider {\n",
      "  constant_value: 10.0\n",
      "}\n",
      "split_after_samples {\n",
      "  constant_value: 250.0\n",
      "}\n",
      "dominate_fraction {\n",
      "  constant_value: 0.9900000095367432\n",
      "}\n",
      "num_features: 16\n",
      ", 'serialized_params_proto': b'\"\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00*\\x07\\n\\x05\\r\\x00\\x00\\x00\\x00028\\xe8\\x07`\\x1aj\\x05\\r\\x00\\x00 Ar\\x05\\r\\x00\\x00zCz\\x05\\r\\xa4p}?\\xa8\\x01\\x10'}\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmp7g1v4ic7/model.ckpt-127\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmphe0viouo/model.ckpt-127\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpbpcp9pzp/model.ckpt-127\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpvw0jmvf9/model.ckpt-127\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpjwskjg3k/model.ckpt-127\n",
      "INFO:tensorflow:dense_features_size: 16 dense: [{name: features original_type: 0 size: 16}] sparse: []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmph0vmjxou/model.ckpt-128\n",
      "Accuracy(layer-0-estimator-6-3folds - train_avg) = 90.7625%\n",
      "Accuracy(layer-0-estimator-7-3folds - train_avg) = 90.4312%\n",
      "Accuracy(layer-0-estimator-6-3folds - test_avg) = 90.9750%\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "Accuracy(layer-0-estimator-7-3folds - test_avg) = 90.7750%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process Process-45:\n",
      "Process Process-44:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpf6qwua0x/model.ckpt-127\n",
      "Accuracy(layer-0-estimator-3-3folds - train_avg) = 90.5875%\n",
      "Accuracy(layer-0-estimator-3-3folds - test_avg) = 90.7500%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process Process-41:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/huqiu/programs/anaconda3/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/huqiu/programs/anaconda3/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/huqiu/programs/anaconda3/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/huqiu/programs/anaconda3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/huqiu/programs/anaconda3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/huqiu/programs/anaconda3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"<ipython-input-27-c2491e70b36f>\", line 90, in worker\n",
      "    y_proba_train, y_proba_test = kfw(name=est_name, n_folds=3,\n",
      "NameError: name 'kfw' is not defined\n",
      "  File \"<ipython-input-27-c2491e70b36f>\", line 90, in worker\n",
      "    y_proba_train, y_proba_test = kfw(name=est_name, n_folds=3,\n",
      "  File \"<ipython-input-27-c2491e70b36f>\", line 90, in worker\n",
      "    y_proba_train, y_proba_test = kfw(name=est_name, n_folds=3,\n",
      "NameError: name 'kfw' is not defined\n",
      "NameError: name 'kfw' is not defined\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "Accuracy(layer-0-estimator-2-3folds - train_avg) = 90.7625%\n",
      "Accuracy(layer-0-estimator-2-3folds - test_avg) = 90.2500%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process Process-40:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/huqiu/programs/anaconda3/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/huqiu/programs/anaconda3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"<ipython-input-27-c2491e70b36f>\", line 90, in worker\n",
      "    y_proba_train, y_proba_test = kfw(name=est_name, n_folds=3,\n",
      "NameError: name 'kfw' is not defined\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy(layer-0-estimator-1-3folds - train_avg) = 90.6625%\n",
      "Accuracy(layer-0-estimator-1-3folds - test_avg) = 90.6000%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process Process-39:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/huqiu/programs/anaconda3/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/huqiu/programs/anaconda3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"<ipython-input-27-c2491e70b36f>\", line 90, in worker\n",
      "    y_proba_train, y_proba_test = kfw(name=est_name, n_folds=3,\n",
      "NameError: name 'kfw' is not defined\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy(layer-0-estimator-0-3folds - train_avg) = 90.7250%\n",
      "Accuracy(layer-0-estimator-0-3folds - test_avg) = 90.7750%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process Process-38:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/huqiu/programs/anaconda3/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/huqiu/programs/anaconda3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"<ipython-input-27-c2491e70b36f>\", line 90, in worker\n",
      "    y_proba_train, y_proba_test = kfw(name=est_name, n_folds=3,\n",
      "NameError: name 'kfw' is not defined\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmprqhgeip1/model.ckpt-127\n",
      "Accuracy(layer-0-estimator-5-3folds - train_avg) = 90.4375%\n",
      "Accuracy(layer-0-estimator-5-3folds - test_avg) = 90.7250%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process Process-43:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/huqiu/programs/anaconda3/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/huqiu/programs/anaconda3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"<ipython-input-27-c2491e70b36f>\", line 90, in worker\n",
      "    y_proba_train, y_proba_test = kfw(name=est_name, n_folds=3,\n",
      "NameError: name 'kfw' is not defined\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "Accuracy(layer-0-estimator-4-3folds - train_avg) = 90.8937%\n",
      "Accuracy(layer-0-estimator-4-3folds - test_avg) = 91.2500%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process Process-42:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/huqiu/programs/anaconda3/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/huqiu/programs/anaconda3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"<ipython-input-27-c2491e70b36f>\", line 90, in worker\n",
      "    y_proba_train, y_proba_test = kfw(name=est_name, n_folds=3,\n",
      "NameError: name 'kfw' is not defined\n"
     ]
    }
   ],
   "source": [
    "for i in range(number_workers):\n",
    "    w_procs[i].start()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for proc in [ps_proc, ] + w_procs:\n",
    "    proc.terminate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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